Meng Jinhui, Zhang Li, Wang Lianxin, Li Shimeng, Xie Di, Zhang Yuxi, Liu Hongsheng
School of Life Science, Liaoning University, Shenyang, 110036, China.
School of Life Science, Liaoning University, Shenyang, 110036, China; Technology Innovation Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China.
Toxicology. 2021 Dec;464:153018. doi: 10.1016/j.tox.2021.153018. Epub 2021 Oct 29.
The human ether-à-go-go-related gene (hERG) encodes the Kv11.1 voltage-gated potassium ion (K) channel that conducts the rapidly activating delayed rectifier current (I) in cardiomyocytes to regulate the repolarization process. Some drugs, as blockers of hERG potassium channels, cannot be marketed due to prolonged QT intervals, as well known as cardiotoxicity. Predetermining the binding affinity values between drugs and hERG through in silico methods can greatly reduce the time and cost required for experimental verification. In this study, we collected 9,215 compounds with AutoDock Vina's docking structures as training set, and collected compounds from four references as test sets. A series of models for predicting the binding affinities of hERG blockers were built based on five machine learning algorithms and combinations of interaction features and ligand features. The model built by support vector regression (SVR) using the combination of all features achieved the best performance on both tenfold cross-validation and external verification, which was selected and named as TSSF-hERG (target-specific scoring function for hERG). TSSF-hERG is more accurate than the classic scoring function of AutoDock Vina and the machine-learning-based generic scoring function RF-Score, with a Pearson's correlation coefficient (Rp) of 0.765, a Spearman's rank correlation coefficient (Rs) of 0.757, a root-mean-square error (RMSE) of 0.585 in a tenfold cross-validation study. All results demonstrated that TSSF-hERG would be useful for improving the power of binding affinity prediction between hERG and compounds, which can be further used for prediction or virtual screening of the hERG-related cardiotoxicity of drug candidates.
人类醚 - 去极化相关基因（hERG）编码Kv11.1电压门控钾离子（K）通道，该通道在心肌细胞中传导快速激活延迟整流电流（I）以调节复极化过程。一些药物作为hERG钾通道阻滞剂，由于QT间期延长（即心脏毒性）而无法上市。通过计算机模拟方法预先确定药物与hERG之间的结合亲和力值，可以大大减少实验验证所需的时间和成本。在本研究中，我们收集了9215种具有AutoDock Vina对接结构的化合物作为训练集，并从四篇参考文献中收集化合物作为测试集。基于五种机器学习算法以及相互作用特征和配体特征的组合，构建了一系列预测hERG阻滞剂结合亲和力的模型。使用所有特征组合通过支持向量回归（SVR）构建的模型在十折交叉验证和外部验证中均表现出最佳性能，该模型被选中并命名为TSSF - hERG（hERG的靶点特异性评分函数）。TSSF - hERG比AutoDock Vina的经典评分函数和基于机器学习的通用评分函数RF - Score更准确，在十折交叉验证研究中，其皮尔逊相关系数（Rp）为0.765，斯皮尔曼等级相关系数（Rs）为0.757，均方根误差（RMSE）为0.585。所有结果表明，TSSF - hERG将有助于提高hERG与化合物之间结合亲和力预测的能力，可进一步用于预测或虚拟筛选候选药物的hERG相关心脏毒性。
